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A Probabilistic Theory of Pattern Recognition
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“To train the PFC classification unit in the model, we used a random splits procedure, which has been shown to provide a good estimate of the expected error of a classifier (56).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [56]
In the citing paper
“It is clearly not, in general, a consistent estimate (Devroye et al., 1996, Chapter 5).”
From A random forest guided tour (2016) · cites this paper as Devroye et al. (1996)
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Metadata
- Authors
- Devroye, L., Györfi, L., & Lugosi, G.
- Affiliations
- McGill University (CA)
- Budapest University of Technology and Economics (HU)
- Venue
- Stochastic Modelling and Applied Probability
- Publisher
- Springer New York
- Published
- 1996
- ISSN
- 2197-4381 (ISSN-L)
- 0172-4568
- 2197-439X
- OpenAlex
- W1564947197
- Cited by
- 3,274
- References
- 6
- Citations by year
- 2026: 31 · 2025: 57 · 2024: 74 · 2023: 69 · 2022: 101 · 2021: 113 · 2020: 131 · 2019: 136 · 2018: 106 · 2017: 98 · 2016: 120 · 2015: 150 · 2014: 125 · 2013: 119 · 2012: 141
- FWCI
- 20.89
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Stochastic modelling and applied probability (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- distance measures, kernel methods, nearest neighbors, Vapnik-Chervonenkis theory, epsilon-entropy, parametric classification, error estimation, classification trees, artificial neural networks
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